SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 751–775 of 1570 papers

TitleStatusHype
Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving—0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
Substructure Aware Graph Neural NetworksCode1
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI—0
Directional diffusion models for graph representation learning—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Spatial-Temporal Graph Learning with Adversarial Contrastive AdaptationCode1
Globally Interpretable Graph Learning via Distribution Matching—0
STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual DiarizationCode1
Multi-Temporal Relationship Inference in Urban AreasCode0
Explainable and Position-Aware Learning in Digital Pathology—0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
Learning on Graphs under Label Noise—0
Time-aware Graph Structure Learning via Sequence Prediction on Temporal GraphsCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Expectation-Complete Graph Representations with HomomorphismsCode0
A Graph Dynamics Prior for Relational InferenceCode0
Comprehensive evaluation of deep and graph learning on drug-drug interactions predictionCode1
arXiv4TGC: Large-Scale Datasets for Temporal Graph ClusteringCode0
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs—0
Permutation Equivariant Graph Framelets for Heterophilous Graph LearningCode0
Migrate Demographic Group For Fair GNNs—0
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free DataCode1
Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified